Exact estimation of multiple directed acyclic graphs

被引：23
|
作者：
Oates, Chris J. [1 ,6 ]
Smith, Jim Q. [1 ]
Mukherjee, Sach [2 ,3 ,7 ]
Cussens, James [4 ,5 ]
机构：
[1] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England
[2] Univ Cambridge, MRC Biostat Unit, Cambridge CB2 0SR, England
[3] Univ Cambridge, CRUK Cambridge Inst, Cambridge CB2 0SR, England
[4] Univ York, Dept Comp Sci, York YO10 5GE, N Yorkshire, England
[5] Univ York, York Ctr Complex Syst Anal, York YO10 5GE, N Yorkshire, England
[6] Univ Technol Sydney, Sch Math & Phys Sci, POB 123, Sydney, NSM 2007, Australia
[7] German Ctr Neurodegenerat Dis DZNE, D-53175 Bonn, Germany
来源：
STATISTICS AND COMPUTING | 2016年 / 26卷 / 04期
基金：
英国医学研究理事会; 英国工程与自然科学研究理事会;
关键词：
Hierarchical model; Bayesian network; Multiregression dynamical model; Integer linear programming; Joint estimation; INVERSE COVARIANCE ESTIMATION; BAYESIAN NETWORKS; EFFECTIVE CONNECTIVITY; RECONSTRUCTION; KNOWLEDGE; INFERENCE;
D O I：
10.1007/s11222-015-9570-9
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.
引用
收藏
页码：797 / 811
页数：15
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